RNA-Seq Blog Transcriptome Research & Industry News
Brain slice from a transgenic mouse, in which genetically defined neurons in the cerebral cortex are labeled with a red fluorescent reporter gene. (Image by Tanya Daigle, courtesy of the Allen Institute)
The BRAIN Initiative Cell Census Network (BICCN) — created in 2017 — endeavors to map all the different cell types throughout the brain, which consists of more than 160 billion individual cells, both neurons and support cells called glia. The BRAIN Initiative was launched in 2013 by then-President Barack Obama.
17 studies, appearing online Oct. 6 in the journal Nature, are the result of five years of work by a huge consortium of researchers supported by the National Institutes of Health’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative to identify the myriad of different cell types in one portion of the brain. It is the first step in a long-term project to generate an atlas of the entire brain to help understand how the neural networks in our head control our body and mind and how they are disrupted in cases of mental and physical problems.
Researchers involved in these studies had collaborated on an earlier study to profile all the active genes in single dopamine-producing cells in the mouse’s midbrain, which has structures similar to human brains. This same profiling technique, which involves identifying all the specific messenger RNA molecules and their levels in each cell, was employed by other BICCN researchers to profile cells in the motor cortex. This type of analysis, using a technique called single-cell RNA sequencing, or scRNA-seq, is referred to as transcriptomics.
The scRNA-seq technique was one of nearly a dozen separate experimental methods used by the BICCN team to characterize the different cell types in three different mammals: mice, marmosets and humans. Four of these involved different ways of identifying gene expression levels and determining the genome’s chromatin architecture and DNA methylation status, which is called the epigenome. Other techniques included classical electrophysiological patch clamp recordings to distinguish cells by how they fire action potentials, categorizing cells by shape, determining their connectivity, and looking at where the cells are spatially located within the brain. Several of these used machine learning or artificial intelligence to distinguish cell types.
“This was the most comprehensive description of these cell types, and with high resolution and different methodologies. The conclusion of the paper is that there’s remarkable overlap and consistency in determining cell types with these different methods.” Dirk Hockemeyer -UC Berkeley
“The big advance by the BICCN is that we combined many different ways of defining a cell type and integrated them to come up with a consensus taxonomy that’s not just based on gene expression or on physiology or morphology, but takes all of those properties into account,” Hockemeyer said. “So, now we can say this particular cell type expresses these genes, has this morphology, has these physiological properties, and is located in this particular region of the cortex. So, you have a much deeper, granular understanding of what that cell type is and its basic properties.”
Source – University of California – Berkeley
